Automatic Segmentation of Thorax CT Images with Fully Convolutional Networks

Mao, Kai-peng; Xie, Sihong; Shao, Wei; Wang, Shawn X.

doi:10.1515/9783110584974-044

Cited by 2 publications

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“…21 As an artificial intelligence (AI) method of mimicking human beings' activity, the fully convolutional network (FCN) is proposed to delineate OARs automatically. [27][28][29][30][31] The first attempt of applying a convolutional neural network (CNN) on OARs' segmentation was reported by Ibragimov and Xing. 31 Their work focused on the OARs in head and neck region.…”

Section: Introductionmentioning

confidence: 99%

“…For the thoracic OARs, a 11-layer FCN was adopted to label the voxels of lung and achieved Dice of 0.96. 28 Dice is the common similarity metric between two images. According to Zhu et al, 29 a 13-layer and 5-channel CNN delineated the spinal cord with a Dice of 0.71–0.79, and lung and heart with a Dice of 0.87–0.95 in a series of images with the size of 96 × 96 pixels.…”

Section: Introductionmentioning

confidence: 99%

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Fully convolutional network-based multi-output model for automatic segmentation of organs at risk in thorax

et al. 2021

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Purpose: To propose a multi-output fully convolutional network (MOFCN) to segment bilateral lung, heart and spinal cord in the planning thoracic computed tomography (CT) slices automatically and simultaneously. Methods: The MOFCN includes two components: one main backbone and three branches. The main backbone extracts the features about lung, heart and spinal cord. The extracted features are transferred to three branches which correspond to three organs respectively. The longest branch to segment spinal cord is nine layers, including input and output layers. The MOFCN was evaluated on 19,277 CT slices from 966 patients with cancer in the thorax. In these slices, the organs at risk (OARs) were delineated and validated by experienced radiation oncologists, and served as ground truth for training and evaluation. The data from 61 randomly chosen patients were used for training and validation. The remaining 905 patients’ slices were used for testing. The metric used to evaluate the similarity between the auto-segmented organs and their ground truth was Dice. Besides, we compared the MOFCN with other published models. To assess the distinct output design and the impact of layer number and dilated convolution, we compared MOFCN with a multi-label learning model and its variants. By analyzing the not good performances, we suggested possible solutions. Results: MOFCN achieved Dice of 0.95 ± 0.02 for lung, 0.91 ± 0.03 for heart and 0.87 ± 0.06 for spinal cord. Compared to other models, MOFCN could achieve a comparable accuracy with the least time cost. Conclusion: The results demonstrated the MOFCN’s effectiveness. It uses less parameters to delineate three OARs simultaneously and automatically, and thus shows a relatively low requirement for hardware and has potential for broad application.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fully convolutional network-based multi-output model for automatic segmentation of organs at risk in thorax

et al. 2021

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Classification of Chest Diseases Using Deep Learning

Silva

Zilberman

Patiño

et al. 2020

Advances in Intelligent Systems and Computing

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The field of computer vision has had exponential progress in a wide range of applications due to the use of deep learning and especially the existence of large annotated image data sets [1]. Significant improvements have been shown in the performance of problems previously considered difficult, such as object recognition, detection and segmentation over approaches based on obtaining the characteristics of the image by hand [2]. This article presents a novel method for the classification of chest diseases in the standard and widely used data set ChestX-ray8, which contains more than 100,000 front view images with 8 diseases.

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Automatic Segmentation of Thorax CT Images with Fully Convolutional Networks

Cited by 2 publications

References 0 publications

Fully convolutional network-based multi-output model for automatic segmentation of organs at risk in thorax

Fully convolutional network-based multi-output model for automatic segmentation of organs at risk in thorax

Classification of Chest Diseases Using Deep Learning

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